



'''simple docstring'''

import os
import shutil
import tempfile
import unittest

import numpy as np

from transformers import AutoTokenizer, BarkProcessor
from transformers.testing_utils import require_torch, slow

@require_torch
class 							a__( unittest.TestCase     ):






			def 						_lowercase  (     self						)							->							Optional[int]:
					snake_case__	     ='ylacombe/bark-small'
					snake_case__	     =tempfile.mkdtemp()
					snake_case__	     ='en_speaker_1'
					snake_case__	     ='This is a test string'
					snake_case__	     ='speaker_embeddings_path.json'
					snake_case__	     ='speaker_embeddings'






			def 						_lowercase  (     self					,   **_UpperCAmelCase						)							->							Dict:
					return AutoTokenizer.from_pretrained(self.checkpoint					,   **_UpperCAmelCase						)






			def 						_lowercase  (     self						)							->							Tuple:
					shutil.rmtree(self.tmpdirname						)






			def 						_lowercase  (     self						)							->							str:
					snake_case__	     =self.get_tokenizer()

					snake_case__	     =BarkProcessor(tokenizer=_UpperCAmelCase						)

					processor.save_pretrained(self.tmpdirname						)
					snake_case__	     =BarkProcessor.from_pretrained(self.tmpdirname						)

					self.assertEqual(processor.tokenizer.get_vocab()					,   tokenizer.get_vocab()						)






			@slow
			def 						_lowercase  (     self						)							->							Optional[Any]:
					snake_case__	     =BarkProcessor.from_pretrained(
					    pretrained_processor_name_or_path=self.checkpoint					,   speaker_embeddings_dict_path=self.speaker_embeddings_dict_path					,   )
					processor.save_pretrained(
					    self.tmpdirname					,   speaker_embeddings_dict_path=self.speaker_embeddings_dict_path					,   speaker_embeddings_directory=self.speaker_embeddings_directory					,   )

					snake_case__	     =self.get_tokenizer(bos_token='(BOS)'					,   eos_token='(EOS)'						)

					snake_case__	     =BarkProcessor.from_pretrained(
					    self.tmpdirname					,   self.speaker_embeddings_dict_path					,   bos_token='(BOS)'					,   eos_token='(EOS)'					,   )

					self.assertEqual(processor.tokenizer.get_vocab()					,   tokenizer_add_kwargs.get_vocab()						)






			def 						_lowercase  (     self						)							->							int:
					snake_case__	     =BarkProcessor.from_pretrained(
					    pretrained_processor_name_or_path=self.checkpoint					,   speaker_embeddings_dict_path=self.speaker_embeddings_dict_path					,   )

					snake_case__	     =35
					snake_case__	     =2
					snake_case__	     =8

					snake_case__	     ={
					    'semantic_prompt': np.ones(_UpperCAmelCase						),
					    'coarse_prompt': np.ones((nb_codebooks_coarse, seq_len)						),
					    'fine_prompt': np.ones((nb_codebooks_total, seq_len)						),
					}

					# test providing already loaded voice_preset
					snake_case__	     =processor(text=self.input_string					,   voice_preset=_UpperCAmelCase						)

					snake_case__	     =inputs['history_prompt']
					for key in voice_preset:
							self.assertListEqual(voice_preset[key].tolist()					,   processed_voice_preset.get(_UpperCAmelCase					,   np.array([]						)						).tolist()						)

					# test loading voice preset from npz file
					snake_case__	     =os.path.join(self.tmpdirname					,   'file.npz'						)
					np.savez(_UpperCAmelCase					,   **_UpperCAmelCase						)
					snake_case__	     =processor(text=self.input_string					,   voice_preset=_UpperCAmelCase						)
					snake_case__	     =inputs['history_prompt']

					for key in voice_preset:
							self.assertListEqual(voice_preset[key].tolist()					,   processed_voice_preset.get(_UpperCAmelCase					,   np.array([]						)						).tolist()						)

					# test loading voice preset from the hub
					snake_case__	     =processor(text=self.input_string					,   voice_preset=self.voice_preset						)






			def 						_lowercase  (     self						)							->							Any:
					snake_case__	     =self.get_tokenizer()

					snake_case__	     =BarkProcessor(tokenizer=_UpperCAmelCase						)

					snake_case__	     =processor(text=self.input_string						)

					snake_case__	     =tokenizer(
					    self.input_string					,   padding='max_length'					,   max_length=256					,   add_special_tokens=_UpperCAmelCase					,   return_attention_mask=_UpperCAmelCase					,   return_token_type_ids=_UpperCAmelCase					,   )

					for key in encoded_tok.keys():
							self.assertListEqual(encoded_tok[key]					,   encoded_processor[key].squeeze().tolist()						)









'''simple docstring'''

import warnings

from ...utils import logging
from .image_processing_glpn import GLPNImageProcessor


SCREAMING_SNAKE_CASE__     :						Optional[Any]	=  logging.get_logger(__name__)

class 							a__( snake_case__     ):






			def __init__(     self					,   *_UpperCAmelCase					,   **_UpperCAmelCase						)							->							None:
					warnings.warn(
					    'The class GLPNFeatureExtractor is deprecated and will be removed in version 5 of Transformers. Please'
					    ' use GLPNImageProcessor instead.'					,   _UpperCAmelCase					,   )
					super().__init__(*_UpperCAmelCase					,   **_UpperCAmelCase						)




